The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds

2026-09-18

AI Summary

研究通过结合几何与辐射特征改进了跨站点LiDAR点云的叶木分割方法，特别是在提高木材召回率和结构连通性方面表现更优。

Abstract

Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomass estimation. Existing segmentation methods typically exclude radiometric features (e.g., intensity, return number) to maximize cross-sensor compatibility. We challenge this design choice by evaluating cross-site and cross-platform generalization: training on the public Heidelberg dataset (terrestrial TLS, 1550nm) and testing on a novel dataset from Ontario, Canada (RPA-LS, 905nm). Results show that geometry-only methods - including state-of-the-art deep learning models trained on high-density LiDAR datasets - fail to generalize to the sparse, top-down geometry of aerial scans, achieving F1 scores <= 0.56. Incorporating radiometric features (intensity, return number, number of returns) improves F1 to 0.61, but more critically, increases wood recall by 119% from 0.16 to 0.35. Furthermore, geometry-only approaches often result in fragmented stem and branch components. We find that leveraging radiometric features preserves greater structural connectivity, resulting in more coherent architectures that are better suited for QSM reconstruction. We demonstrate that while geometric patterns are view-dependent and prone to overfitting scan patterns, radiometric features encode physical material properties that generalize across disparate sensors and environments.

Problem

• Leaf-wood segmentation
• Radiometric features
• Cross-site generalization
• LiDAR point clouds

Innovation

• radiometric features
• cross-site generalization
• LiDAR point clouds
• leaf-wood segmentation